Steps

0. Install and load libraries

1. Read in the data

There are 4 datasets to read in, Starbucks locations, Starbucks nutrition, US population by state, and US state abbreviations. All of them are on the course GitHub.

sb_locs <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/starbucks-locations.csv")

## Rows: 25600 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): Store Number, Store Name, Ownership Type, Street Address, City, St...
## dbl  (2): Longitude, Latitude
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

sb_nutr <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/starbucks-menu-nutrition.csv")

## Rows: 205 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Item, Category
## dbl (5): Calories, Fat (g), Carb. (g), Fiber (g), Protein (g)
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

usa_pop <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/us_state_pop.csv")

## Rows: 55 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (1): population
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

usa_states<-read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/states.csv")

## Rows: 51 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): State, Abbreviation
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

2. Look at the data

Inspect each dataset to look at variable names and ensure it was imported correctly.

sb_locs

## # A tibble: 25,600 × 12
##    `Store Number` `Store Name`           `Ownership Type` `Street Address` City 
##    <chr>          <chr>                  <chr>            <chr>            <chr>
##  1 47370-257954   Meritxell, 96          Licensed         Av. Meritxell, … Ando…
##  2 22331-212325   Ajman Drive Thru       Licensed         1 Street 69, Al… Ajman
##  3 47089-256771   Dana Mall              Licensed         Sheikh Khalifa … Ajman
##  4 22126-218024   Twofour 54             Licensed         Al Salam Street  Abu …
##  5 17127-178586   Al Ain Tower           Licensed         Khaldiya Area, … Abu …
##  6 17688-182164   Dalma Mall, Ground Fl… Licensed         Dalma Mall, Mus… Abu …
##  7 18182-182165   Dalma Mall, Level 1    Licensed         Dalma Mall, Mus… Abu …
##  8 23359-229184   Debenhams Yas Mall     Licensed         Yas Island       Abu …
##  9 30781-99022    Khalidiya Street       Licensed         Khalidiya St.    Abu …
## 10 20423-205465   Eastern Mangroves      Licensed         Al Salam Street… Abu …
## # ℹ 25,590 more rows
## # ℹ 7 more variables: `State/Province` <chr>, Country <chr>, Postcode <chr>,
## #   `Phone Number` <chr>, Timezone <chr>, Longitude <dbl>, Latitude <dbl>

sb_nutr

## # A tibble: 205 × 7
##    Item        Category Calories `Fat (g)` `Carb. (g)` `Fiber (g)` `Protein (g)`
##    <chr>       <chr>       <dbl>     <dbl>       <dbl>       <dbl>         <dbl>
##  1 Chonga Bag… Food          300         5          50           3            12
##  2 8-Grain Ro… Food          380         6          70           7            10
##  3 Almond Cro… Food          410        22          45           3            10
##  4 Apple Frit… Food          460        23          56           2             7
##  5 Banana Nut… Food          420        22          52           2             6
##  6 Blueberry … Food          380        16          53           1             6
##  7 Blueberry … Food          420        17          61           2             5
##  8 Butter Cro… Food          240        12          28           1             5
##  9 Butterfly … Food          350        22          38           0             2
## 10 Cheese Dan… Food          320        16          36           1             8
## # ℹ 195 more rows

usa_pop

## # A tibble: 55 × 2
##    state                population
##    <chr>                     <dbl>
##  1 Alabama                 4779736
##  2 Alaska                   710231
##  3 Arizona                 6392017
##  4 Arkansas                2915918
##  5 California             37253956
##  6 Colorado                5029196
##  7 Connecticut             3574097
##  8 Delaware                 897934
##  9 District of Columbia     601723
## 10 Florida                18801310
## # ℹ 45 more rows

usa_states

## # A tibble: 51 × 2
##    State                Abbreviation
##    <chr>                <chr>       
##  1 Alabama              AL          
##  2 Alaska               AK          
##  3 Arizona              AZ          
##  4 Arkansas             AR          
##  5 California           CA          
##  6 Colorado             CO          
##  7 Connecticut          CT          
##  8 Delaware             DE          
##  9 District of Columbia DC          
## 10 Florida              FL          
## # ℹ 41 more rows

3. Format and merge the data

Subset Starbucks data to the US.
Create counts of Starbucks stores by state.
Merge population in with the store count by state.
Inspect the range values for each variable.

# Subset Starbucks locations to only those in the US
sb_usa <- sb_locs |> filter(Country == "US")

# Count Starbucks stores by state
sb_locs_state <- sb_usa |>
  group_by(`State/Province`) |>
  summarize(store_count = n())

# Rename the state abbreviation column in the states dataset
usa_states <- usa_states |> rename(state = State, abbreviation = Abbreviation)

# Merge state population data with state abbreviations
usa_pop_abbr <- full_join(usa_pop, usa_states, by = c("state" = "state"))

# Merge the Starbucks store count data with the population dataset
sb_locs_state <- full_join(sb_locs_state, usa_pop_abbr, 
                           by = c("State/Province" = "abbreviation"))

# Inspect the range of values
summary(sb_locs_state)

##  State/Province      store_count        state             population      
##  Length:55          Min.   :   8.0   Length:55          Min.   :   56882  
##  Class :character   1st Qu.:  56.5   Class :character   1st Qu.: 1344331  
##  Mode  :character   Median : 123.0   Mode  :character   Median : 3751351  
##                     Mean   : 266.8                      Mean   : 5677621  
##                     3rd Qu.: 332.0                      3rd Qu.: 6515716  
##                     Max.   :2821.0                      Max.   :37253956  
##                     NA's   :4

4. Use `ggplotly` for EDA

Answer the following questions:

Are the number of Starbucks proportional to the population of a state? (scatterplot)
Is the caloric distribution of Starbucks menu items different for drinks and food? (histogram)
What are the top 20 words in Starbucks menu items? (bar plot)

# Scatterplot of Starbucks stores vs. population
p1 <- sb_locs_state |> 
  ggplot(aes(x = population, y = store_count, text = state)) + 
  geom_point(color = "darkgreen", alpha = 0.6) + 
  geom_smooth(method = "lm", se = FALSE, color = "blue") +
  labs(title = "Starbucks Stores vs. State Population",
       x = "Population",
       y = "Number of Starbucks Stores") +
  theme_minimal()

ggplotly(p1, tooltip = "text")

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 4 rows containing non-finite outside the scale range
## (`stat_smooth()`).

4a) Answer: There is a strong positive correlation between population and number of starbucks stores.

# Ensure there's a category column to distinguish drinks vs. food
p2 <- sb_nutr |> 
  ggplot(aes(x = Calories, fill = Category)) + 
  geom_histogram(bins = 30, position = "identity", alpha = 0.6) + 
  labs(title = "Caloric Distribution: Drinks vs. Food",
       x = "Calories",
       y = "Count",
       fill = "Category") +
  theme_minimal()

ggplotly(p2)

4b) Answer: Food, on average, has higher calories. There is a large amount of drinks with 0 calories.

# Tokenizing menu items into words
top_words <- sb_nutr |> 
  unnest_tokens(word, `Item`) |>  # Assuming 'Beverage_category' contains item names
  anti_join(stop_words, by = "word") |>  # Remove common stopwords
  count(word, sort = TRUE) |> 
  slice_max(n, n = 20)

# Bar plot of top 20 words
p3 <- top_words |> 
  ggplot(aes(x = reorder(word, n), y = n, fill = word)) + 
  geom_col(show.legend = FALSE) + 
  coord_flip() +
  labs(title = "Top 20 Words in Starbucks Menu Items",
       x = "Word",
       y = "Count") +
  theme_minimal()

ggplotly(p3)

4c) Answer: Top 20 words are iced, tazo, bottled, sandwich, chocolate, coffee, tea, starbucks, egg, protein, cake, black, white, mocha, latte, cookie, chicken, vanilla, salad, shaken, organic, and macchiato.

5. Scatterplots using `plot_ly()`

Create a scatterplot using plot_ly() representing the relationship between calories and carbs. Color the points by category (food or beverage). Is there a relationship, and do food or beverages tend to have more calories?

# Scatterplot of Calories vs. Carbs using plot_ly
plot_ly(data = sb_nutr, 
        x = ~`Carb. (g)`, 
        y = ~Calories, 
        color = ~Category,  # Coloring by food or beverage category
        type = "scatter", 
        mode = "markers",
        marker = list(size = 8, opacity = 0.6)) %>%
  layout(title = "Calories vs. Carbohydrates in Starbucks Menu Items",
         xaxis = list(title = "Carbohydrates (g)"),
         yaxis = list(title = "Calories"),
         legend = list(title = list(text = "Category")))

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

5a) Answer: There seems to be a positive correlation betweenc carbs and calories. Food, on average, has higher calories than drinks.
Repeat this scatterplot but for the items that include the top 10 words. Color again by category, and add hoverinfo specifying the word in the item name. Add layout information to title the chart and the axes, and enable hovermode = "compare".
What are the top 10 words and is the plot much different than above?

# Tokenizing menu items into words and counting occurrences
top_10_words <- sb_nutr |> 
  unnest_tokens(word, `Item`) |>  # Assuming 'Beverage_category' contains item names
  anti_join(stop_words, by = "word") |>  # Remove common stopwords
  count(word, sort = TRUE) |> 
  slice_max(n, n = 10) |> 
  pull(word)  # Extract as a vector

# Create a regex pattern to match any of the top 10 words
pattern <- paste0("\\b(", paste(top_10_words, collapse = "|"), ")\\b")

# Filter for menu items that contain at least one of the top 10 words
sb_nutr_filtered <- sb_nutr |> 
  filter(str_detect(`Item`, regex(pattern, ignore_case = TRUE))) |> 
  mutate(matching_word = str_extract(`Item`, regex(pattern, ignore_case = TRUE)))


plot_ly(data = sb_nutr_filtered, 
        x = ~`Carb. (g)`, 
        y = ~Calories, 
        color = ~Category,  # Coloring by food or beverage category
        text = ~paste("Word:", matching_word, "<br>Item:", `Item`), 
        type = "scatter", 
        mode = "markers",
        marker = list(size = 8, opacity = 0.7)) %>%
  layout(title = "Calories vs. Carbohydrates for Items Containing Top 10 Words",
         xaxis = list(title = "Carbohydrates (g)"),
         yaxis = list(title = "Calories"),
         legend = list(title = list(text = "Category")),
         hovermode = "compare")  # Enables comparing hover tooltips

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

5b) Answer: The same trend is present.

6. `plot_ly` Boxplots

Create a boxplot of all of the nutritional variables in groups by the 10 item words.
Which top word has the most calories? Which top word has the most protein?

# Convert data to long format for boxplot visualization
sb_nutr_long <- sb_nutr_filtered |> 
  select(matching_word, Calories, `Fat (g)`, `Carb. (g)`, `Fiber (g)`, `Protein (g)`) |>  # Select only nutrition columns
  pivot_longer(cols = Calories:`Protein (g)`,  # Convert all nutrient columns into long format
               names_to = "Nutrient", 
               values_to = "Value")
plot_ly() |> 
  # Boxplot for Calories (Primary Y-Axis)
  add_trace(data = sb_nutr_long |> filter(Nutrient == "Calories"), 
            x = ~matching_word, 
            y = ~Value, 
            type = "box", 
            name = "Calories",
            marker = list(color = "red"),
            yaxis = "y1") |> 
  
  # Boxplots for Other Nutrients (Secondary Y-Axis)
  add_trace(data = sb_nutr_long |> filter(Nutrient != "Calories"), 
            x = ~matching_word, 
            y = ~Value, 
            type = "box", 
            name = ~Nutrient,
            color = ~Nutrient,
            yaxis = "y2") |> 

  layout(title = "Nutritional Distribution of Top 10 Starbucks Menu Words",
         xaxis = list(title = "Top 10 Words in Menu Items"),
         
         # Primary Y-Axis (Calories)
         yaxis = list(title = "Calories", 
                      side = "left",
                      showgrid = FALSE),
         
         # Secondary Y-Axis (Other Nutrients)
         yaxis2 = list(title = "Other Nutrients (g)", 
                       overlaying = "y", 
                       side = "right",
                       showgrid = FALSE),
         
         boxmode = "group")

## Warning: 'layout' objects don't have these attributes: 'boxmode'
## Valid attributes include:
## '_deprecated', 'activeshape', 'annotations', 'autosize', 'autotypenumbers', 'calendar', 'clickmode', 'coloraxis', 'colorscale', 'colorway', 'computed', 'datarevision', 'dragmode', 'editrevision', 'editType', 'font', 'geo', 'grid', 'height', 'hidesources', 'hoverdistance', 'hoverlabel', 'hovermode', 'images', 'legend', 'mapbox', 'margin', 'meta', 'metasrc', 'modebar', 'newshape', 'paper_bgcolor', 'plot_bgcolor', 'polar', 'scene', 'selectdirection', 'selectionrevision', 'separators', 'shapes', 'showlegend', 'sliders', 'smith', 'spikedistance', 'template', 'ternary', 'title', 'transition', 'uirevision', 'uniformtext', 'updatemenus', 'width', 'xaxis', 'yaxis', 'barmode', 'bargap', 'mapType'

1. Answer: Sandwich has the most calories and the most protein.

7. 3D Scatterplot

Create a 3D scatterplot between Calories, Carbs, and Protein for the items containing the top 10 words
Do you see any patterns (clusters or trends)?

# Create a 3D scatterplot
plot_ly(data = sb_nutr_filtered, 
        x = ~`Carb. (g)`, 
        y = ~Calories, 
        z = ~`Protein (g)`, 
        color = ~matching_word,  # Color by top word
        type = "scatter3d", 
        mode = "markers", 
        marker = list(size = 5, opacity = 0.6)) %>%
  layout(title = "3D Scatterplot of Calories, Carbs, and Protein for Top 10 Words",
         scene = list(
           xaxis = list(title = "Carbohydrates (g)"),
           yaxis = list(title = "Calories"),
           zaxis = list(title = "Protein (g)")
         ))

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

1. Answer: There is a clear positive correlation between calories, protein, and carbs, and a small cluster of items with 0 proteins having a positive correlation ebtween calories and carbs.

8. `plot_ly` Map

Create a map to visualize the number of stores per state, and another for the population by state. Add custom hover text. Use subplot to put the maps side by side.
Describe the differences if any.

# Create hover text
# Create hover text
sb_locs_state$hover <- with(sb_locs_state, paste("Number of Starbucks: ", store_count, '<br>', "State: ", state, '<br>', "Population: ", population))


# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('steelblue')
)

# Make sure both maps are on the same color scale
shadeLimit <- 125


# Create the map

# Create a map for the number of stores
map1 <- plot_geo(sb_locs_state, locationmode = 'USA-states') %>%
  add_trace(
    z = ~store_count,
    locations = ~`State/Province`,
    color = ~store_count,
    colors = 'Reds',
    hoverinfo = 'text',
    text = ~hover
  ) %>%
  layout(
    title = "Number of Starbucks Stores per State",
    geo = set_map_details
  )
# Create a map for the population
map2 <- plot_geo(sb_locs_state, locationmode = 'USA-states') %>%
  add_trace(
    z = ~population,
    locations = ~`State/Province`,
    color = ~population,
    colors = 'Blues',
    hoverinfo = 'text',
    text = ~hover
  ) %>%
  layout(
    title = "Population per State",
    geo = set_map_details
  )
# Place maps side by side
subplot(map1, map2, nrows = 1, shareX = TRUE, shareY = TRUE) %>%
  layout(
    title = "Starbucks Stores and Population by State"
  )

## Warning: Ignoring 4 observations

1. Answer: The maps seem to follow the same pattern, with California having a large population as well as a large number of stores.

Lab 11 - Interactive Visualization

Learning Goals

Lab Description

Deliverables

Steps

0. Install and load libraries

1. Read in the data

2. Look at the data

3. Format and merge the data

4. Use `ggplotly` for EDA

5. Scatterplots using `plot_ly()`

6. `plot_ly` Boxplots

7. 3D Scatterplot

8. `plot_ly` Map

Lab 11 - Interactive Visualization

Learning Goals

Lab Description

Deliverables

Steps

0. Install and load libraries

1. Read in the data

2. Look at the data

3. Format and merge the data

4. Use ggplotly for EDA

5. Scatterplots using plot_ly()

6. plot_ly Boxplots

7. 3D Scatterplot

8. plot_ly Map

4. Use `ggplotly` for EDA

5. Scatterplots using `plot_ly()`

6. `plot_ly` Boxplots

8. `plot_ly` Map